ADANA: Active Name Disambiguation. - Xuezhi Wang 002; Jie Tang 001; Hong Cheng 001; Philip S. Yu (ICDM, 2011)

Active search on graphs. - Xuezhi Wang 002; Roman Garnett; Jeff G. Schneider (KDD, 2013)

Active Transfer Learning under Model Shift. - Xuezhi Wang 002; Tzu-Kuo Huang; Jeff G. Schneider (ICML, 2014)

Flexible Transfer Learning under Support and Model Shift. - Xuezhi Wang 002; Jeff G. Schneider (NIPS, 2014)

Biperpedia: An Ontology for Search Applications. - Rahul Gupta; Alon Y. Halevy; Xuezhi Wang 002; Steven Euijong Whang; Fei Wu 003 (Proc. VLDB Endow., 2014)

Generalization Bounds for Transfer Learning under Model Shift. - Xuezhi Wang 002; Jeff G. Schneider (UAI, 2015)

Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems. - Xuezhi Wang 002; Junier B. Oliva; Jeff G. Schneider; Barnabás Póczos (IJCAI, 2016)

Relevant Document Discovery for Fact-Checking Articles. - Xuezhi Wang 002; Cong Yu 001; Simon Baumgartner; Flip Korn (WWW (Companion Volume), 2018)

Contextual Fact Ranking and Its Applications in Table Synthesis and Compression. - Silu Huang; Jialu Liu; Flip Korn; Xuezhi Wang 002; You Wu 001; Dale Markowitz; Cong Yu 001 (KDD, 2019)

Summarizing News Articles Using Question-and-Answer Pairs via Learning. - Xuezhi Wang 002; Cong Yu 001 (ISWC (1), 2019)

Verifying Text Summaries of Relational Data Sets. - Saehan Jo; Immanuel Trummer; Weicheng Yu; Xuezhi Wang 002; Cong Yu 001; Daniel Liu; Niyati Mehta (SIGMOD Conference, 2019)

Automatically Generating Interesting Facts from Wikipedia Tables. - Flip Korn; Xuezhi Wang 002; You Wu 001; Cong Yu 001 (SIGMOD Conference, 2019)

Transfer of Machine Learning Fairness across Domains. - Candice Schumann; Xuezhi Wang 002; Alex Beutel; Jilin Chen; Hai Qian; Ed H. Chi (CoRR, 2019)

Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems. - Xuezhi Wang 002; Nithum Thain; Anu Sinha; Ed H. Chi; Jilin Chen; Alex Beutel (CoRR, 2019)

AggChecker: A Fact-Checking System for Text Summaries of Relational Data Sets. - Saehan Jo; Immanuel Trummer; Weicheng Yu; Xuezhi Wang 002; Cong Yu 001; Daniel Liu; Niyati Mehta (Proc. VLDB Endow., 2019)

Mining an "Anti-Knowledge Base" from Wikipedia Updates with Applications to Fact Checking and Beyond. - Georgios Karagiannis; Immanuel Trummer; Saehan Jo; Shubham Khandelwal; Xuezhi Wang 002; Cong Yu 001 (Proc. VLDB Endow., 2019)

ToTTo: A Controlled Table-To-Text Generation Dataset. - Ankur P. Parikh; Xuezhi Wang 002; Sebastian Gehrmann; Manaal Faruqui; Bhuwan Dhingra; Diyi Yang; Dipanjan Das 001 (EMNLP (1), 2020)

CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation. - Tianlu Wang; Xuezhi Wang 002; Yao Qin 001; Ben Packer; Kang Li; Jilin Chen; Alex Beutel; Ed H. Chi (EMNLP (1), 2020)

Fairness without Demographics through Adversarially Reweighted Learning. - Preethi Lahoti; Alex Beutel; Jilin Chen; Kang Lee; Flavien Prost; Nithum Thain; Xuezhi Wang 002; Ed H. Chi (NeurIPS, 2020)

Improving Uncertainty Estimates through the Relationship with Adversarial Robustness. - Yao Qin 001; Xuezhi Wang 002; Alex Beutel; Ed H. Chi (CoRR, 2020)

Measuring and Reducing Gendered Correlations in Pre-trained Models. - Kellie Webster; Xuezhi Wang 002; Ian Tenney; Alex Beutel; Emily Pitler; Ellie Pavlick; Jilin Chen; Slav Petrov (CoRR, 2020)

Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective. - Flavien Prost; Pranjal Awasthi; Nick Blumm; Aditee Kumthekar; Trevor Potter; Li Wei; Xuezhi Wang 002; Ed H. Chi; Jilin Chen; Alex Beutel (AIES, 2021)

Can We Improve Model Robustness through Secondary Attribute Counterfactuals? - Ananth Balashankar; Xuezhi Wang 002; Ben Packer; Nithum Thain; Ed H. Chi; Alex Beutel (EMNLP (1), 2021)

Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. - Pranjal Awasthi; Alex Beutel; Matthäus Kleindessner; Jamie Morgenstern; Xuezhi Wang 002 (FAccT, 2021)

Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning. - Yuyan Wang; Xuezhi Wang 002; Alex Beutel; Flavien Prost; Jilin Chen; Ed H. Chi (KDD, 2021)

Continual Learning for Text Classification with Information Disentanglement Based Regularization. - Yufan Huang; Yanzhe Zhang; Jiaao Chen; Xuezhi Wang 002; Diyi Yang (NAACL-HLT, 2021)

Improving Calibration through the Relationship with Adversarial Robustness. - Yao Qin 001; Xuezhi Wang 002; Alex Beutel; Ed H. Chi (NeurIPS, 2021)

Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems. - Xuezhi Wang 002; Nithum Thain; Anu Sinha; Flavien Prost; Ed H. Chi; Jilin Chen; Alex Beutel (WSDM, 2021)

Measuring Recommender System Effects with Simulated Users. - Sirui Yao; Yoni Halpern; Nithum Thain; Xuezhi Wang 002; Kang Lee; Flavien Prost; Ed H. Chi; Jilin Chen; Alex Beutel (CoRR, 2021)

Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models. - Tianlu Wang; Diyi Yang; Xuezhi Wang 002 (CoRR, 2021)

Continual Sequence Generation with Adaptive Compositional Modules. - Yanzhe Zhang; Xuezhi Wang 002; Diyi Yang (ACL (1), 2022)

Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers. - Jieyu Zhao 001; Xuezhi Wang 002; Yao Qin 001; Jilin Chen; Kai-Wei Chang (EMNLP (Findings), 2022)

Measure and Improve Robustness in NLP Models: A Survey. - Xuezhi Wang 002; Haohan Wang; Diyi Yang (NAACL-HLT, 2022)

Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models. - Tianlu Wang; Rohit Sridhar; Diyi Yang; Xuezhi Wang 002 (NAACL-HLT (Findings), 2022)

Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. - Yao Qin 001; Chiyuan Zhang; Ting Chen; Balaji Lakshminarayanan; Alex Beutel; Xuezhi Wang 002 (NeurIPS, 2022)

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. - Jason Wei; Xuezhi Wang 002; Dale Schuurmans; Maarten Bosma; Brian Ichter; Fei Xia 002; Ed H. Chi; Quoc V. Le; Denny Zhou (NeurIPS, 2022)

Chain of Thought Prompting Elicits Reasoning in Large Language Models. - Jason Wei; Xuezhi Wang 002; Dale Schuurmans; Maarten Bosma; Ed H. Chi; Quoc Le 001; Denny Zhou (CoRR, 2022)

Self-Consistency Improves Chain of Thought Reasoning in Language Models. - Xuezhi Wang 002; Jason Wei; Dale Schuurmans; Quoc V. Le; Ed H. Chi; Denny Zhou (CoRR, 2022)

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. - Denny Zhou; Nathanael Schärli; Le Hou; Jason Wei; Nathan Scales; Xuezhi Wang 002; Dale Schuurmans; Olivier Bousquet; Quoc Le 001; Ed H. Chi (CoRR, 2022)

Rationale-Augmented Ensembles in Language Models. - Xuezhi Wang 002; Jason Wei; Dale Schuurmans; Quoc V. Le; Ed H. Chi; Denny Zhou (CoRR, 2022)

Scaling Instruction-Finetuned Language Models. - Hyung Won Chung; Le Hou; Shayne Longpre; Barret Zoph; Yi Tay; William Fedus; Eric Li; Xuezhi Wang 002; Mostafa Dehghani 001; Siddhartha Brahma; Albert Webson; Shixiang Shane Gu; et al. (CoRR, 2022)

TEMPERA: Test-Time Prompting via Reinforcement Learning. - Tianjun Zhang; Xuezhi Wang 002; Denny Zhou; Dale Schuurmans; Joseph E. Gonzalez (CoRR, 2022)

Underspecification Presents Challenges for Credibility in Modern Machine Learning. - Alexander D'Amour; Katherine A. Heller; Dan Moldovan; Ben Adlam; Babak Alipanahi; Alex Beutel; Christina Chen; Jonathan Deaton; Jacob Eisenstein; Matthew D. Hoffman; Farhad Hormozdiari; Neil Houlsby; et al. (J. Mach. Learn. Res., 2022)

A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures. - Amanda Baughan; Xuezhi Wang 002; Ariel Liu; Allison Mercurio; Jilin Chen; Xiao Ma 010 (CHI, 2023)

Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints. - Albert Lu; Hongxin Zhang 005; Yanzhe Zhang; Xuezhi Wang 002; Diyi Yang (EACL (Findings), 2023)

Large Language Models Can Self-Improve. - Jiaxin Huang 001; Shixiang Gu; Le Hou; Yuexin Wu; Xuezhi Wang 002; Hongkun Yu 001; Jiawei Han 001 (EMNLP, 2023)

Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation. - Ananth Balashankar; Xuezhi Wang 002; Yao Qin 001; Ben Packer; Nithum Thain; Ed H. Chi; Jilin Chen; Alex Beutel (EMNLP (Findings), 2023)

Self-Consistency Improves Chain of Thought Reasoning in Language Models. - Xuezhi Wang 002; Jason Wei; Dale Schuurmans; Quoc V. Le; Ed H. Chi; Sharan Narang; Aakanksha Chowdhery; Denny Zhou (ICLR, 2023)

Language models are multilingual chain-of-thought reasoners. - Freda Shi; Mirac Suzgun; Markus Freitag; Xuezhi Wang 002; Suraj Srivats; Soroush Vosoughi; Hyung Won Chung; Yi Tay; Sebastian Ruder; Denny Zhou; Dipanjan Das 001; Jason Wei (ICLR, 2023)

Recitation-Augmented Language Models. - Zhiqing Sun; Xuezhi Wang 002; Yi Tay; Yiming Yang; Denny Zhou (ICLR, 2023)

UL2: Unifying Language Learning Paradigms. - Yi Tay; Mostafa Dehghani 001; Vinh Q. Tran 002; Xavier Garcia; Jason Wei; Xuezhi Wang 002; Hyung Won Chung; Dara Bahri; Tal Schuster; Huaixiu Steven Zheng; Denny Zhou; Neil Houlsby; et al. (ICLR, 2023)

TEMPERA: Test-Time Prompt Editing via Reinforcement Learning. - Tianjun Zhang; Xuezhi Wang 002; Denny Zhou; Dale Schuurmans; Joseph E. Gonzalez (ICLR, 2023)

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. - Denny Zhou; Nathanael Schärli; Le Hou; Jason Wei; Nathan Scales; Xuezhi Wang 002; Dale Schuurmans; Claire Cui; Olivier Bousquet; Quoc V. Le; Ed H. Chi (ICLR, 2023)

Grammar Prompting for Domain-Specific Language Generation with Large Language Models. - Bailin Wang; Zi Wang; Xuezhi Wang 002; Yuan Cao; Rif A. Saurous; Yoon Kim (NeurIPS, 2023)

What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. - Yao Qin 001; Xuezhi Wang 002; Balaji Lakshminarayanan; Ed H. Chi; Alex Beutel (SaTML, 2023)

Towards Robust Prompts on Vision-Language Models. - Jindong Gu; Ahmad Beirami; Xuezhi Wang 002; Alex Beutel; Philip H. S. Torr; Yao Qin 001 (CoRR, 2023)

Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals. - Ananth Balashankar; Xuezhi Wang 002; Yao Qin 001; Ben Packer; Nithum Thain; Jilin Chen; Ed H. Chi; Alex Beutel (CoRR, 2023)

Universal Self-Consistency for Large Language Model Generation. - Xinyun Chen; Renat Aksitov; Uri Alon 002; Jie Ren; Kefan Xiao; Pengcheng Yin; Sushant Prakash; Charles Sutton; Xuezhi Wang 002; Denny Zhou (CoRR, 2023)

PaLM: Scaling Language Modeling with Pathways. - Aakanksha Chowdhery; Sharan Narang; Jacob Devlin; Maarten Bosma; Gaurav Mishra; Adam Roberts; Paul Barham 001; Hyung Won Chung; Charles Sutton; Sebastian Gehrmann; Parker Schuh; Kensen Shi; et al. (J. Mach. Learn. Res., 2023)

FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation. - Tu Vu; Mohit Iyyer; Xuezhi Wang 002; Noah Constant; Jerry W. Wei; Jason Wei; Chris Tar; Yun-Hsuan Sung; Denny Zhou; Quoc V. Le; Thang Luong (ACL (Findings), 2024)

Comprehensive Metapath-based Heterogeneous Graph Transformer for Gene-Disease Association Prediction. - Wentao Cui; Shoubo Li; Chen Fang; Qingqing Long; Chengrui Wang; Xuezhi Wang 002; Yuanchun Zhou (BIBM, 2024)

Large Language Models as Tool Makers. - Tianle Cai; Xuezhi Wang 002; Tengyu Ma 001; Xinyun Chen; Denny Zhou (ICLR, 2024)

Large Language Models as Optimizers. - Chengrun Yang; Xuezhi Wang 002; Yifeng Lu; Hanxiao Liu; Quoc V. Le; Denny Zhou; Xinyun Chen (ICLR, 2024)

Premise Order Matters in Reasoning with Large Language Models. - Xinyun Chen; Ryan A. Chi; Xuezhi Wang 002; Denny Zhou (ICML, 2024)

Chain-of-Thought Reasoning Without Prompting. - Xuezhi Wang 002; Denny Zhou (NeurIPS, 2024)

Transformers Can Achieve Length Generalization But Not Robustly. - Yongchao Zhou; Uri Alon 002; Xinyun Chen; Xuezhi Wang 002; Rishabh Agarwal; Denny Zhou (CoRR, 2024)

A Minimalist Prompt for Zero-Shot Policy Learning. - Meng Song; Xuezhi Wang 002; Tanay Biradar; Yao Qin 001; Manmohan Chandraker (CoRR, 2024)

Scaling Instruction-Finetuned Language Models. - Hyung Won Chung; Le Hou; Shayne Longpre; Barret Zoph; Yi Tay; William Fedus; Yunxuan Li; Xuezhi Wang 002; Mostafa Dehghani 001; Siddhartha Brahma; Albert Webson; Shixiang Shane Gu; et al. (J. Mach. Learn. Res., 2024)